The ultimate objective of any geophysical experiment is to find properties of a formation using the measured data. To accomplish the objective a processing technique, called inversion, is applied. Inversion requires a mathematical model, which is used to produce synthetic data. The model has to include all of the necessary parameters that affect the measurements because an accurate model is needed for a successful inversion. Because the amount of recordable data is limited, no geophysical inverse problem is uniquely solvable and inferences of formation properties are statistical.
The possibility to reduce the range of uncertainty in the inverted models is driven by the sensitivity of the data to parameters of interest and the level of noise in the data. The noise can be either random or systematic. Because of this, mechanisms for improving sensitivity to the parameters of a formation have been developed. For example, a technique referred to as bucking was developed to improve sensitivity to the parameters of the formation while eliminating systematic noise caused by a primary field and/or the conductive tools used to make the measurements. If bucking does not account for electromagnetic interference between a conductive tool body and a formation, this interference will create or be a source of additional systematic noise. For this reason, it is beneficial to develop techniques for reducing inconsistency between measured and synthetic model responses and thus reduce uncertainty in estimated parameters of a formation.